在海量数据集中寻找变化状态的活动星系核-- I：应用深度学习和异常检测技术寻找具有异常变化行为的活动星系核
Searching for changing-state AGNs in massive datasets -- I: applying deep learning and anomaly detection techniques to find AGNs with anomalous variability behaviours
摘要：
活动星系核（AGN）的经典分类方案最近受到了所谓的变态（变貌）AGN（CSAGNs）的发现的挑战。这一现象背后的物理机制仍然是一个公开辩论的问题，样本太小，而且是偶然发现的，无法提供可靠的答案。为了解决这个问题，我们需要设计能够在改变状态的行为中检测AGN的方法。在这里，我们提出了一个异常检测（AD）技术，旨在识别活动星系核的光变曲线与异常行为在大量的数据集。这项技术的主要目的是在转变的不同阶段识别CSAGN，但它也可以用于更一般的目的，例如清理AGN可变性分析的大规模数据集。我们使用的光变曲线来自Zwicky Transient Facility数据发布5（ZTF DR 5），包含230，451个不同类别的AGN样本。ZTF DR 5光变曲线采用变分递归自动编码器（VRAE）架构建模，这使我们能够从VRAE潜在空间中获得一组属性，这些属性描述了我们样本的一般行为。然后，这些属性被用作隔离森林（IF）算法的特征，该算法是针对“一类”问题的异常检测器。我们使用VRAE重建误差和IF异常评分来选择8，809个异常的样本。这些异常主要是由虚假的候选人，但我们能够确定75个有希望的CSAGN候选人。
英文摘要：
The classic classification scheme for Active Galactic Nuclei (AGNs) was recently challenged by the discovery of the so-called changing-state (changing-look) AGNs (CSAGNs). The physical mechanism behind this phenomenon is still a matter of open debate and the samples are too small and of serendipitous nature to provide robust answers. In order to tackle this problem, we need to design methods that are able to detect AGN right in the act of changing-state. Here we present an anomaly detection (AD) technique designed to identify AGN light curves with anomalous behaviors in massive datasets. The main aim of this technique is to identify CSAGN at different stages of the transition, but it can also be used for more general purposes, such as cleaning massive datasets for AGN variability analyses. We used light curves from the Zwicky Transient Facility data release 5 (ZTF DR5), containing a sample of 230,451 AGNs of different classes. The ZTF DR5 light curves were modeled with a Variational Recurrent Autoencoder (VRAE) architecture, that allowed us to obtain a set of attributes from the VRAE latent space that describes the general behaviour of our sample. These attributes were then used as features for an Isolation Forest (IF) algorithm, that is an anomaly detector for a "one class" kind of problem. We used the VRAE reconstruction errors and the IF anomaly score to select a sample of 8,809 anomalies. These anomalies are dominated by bogus candidates, but we were able to identify 75 promising CSAGN candidates.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
